Schema evolution

ABSTRACT

Techniques for schema mismatch detection and evolution are described. When data is being uploaded into a source table, schema of the data to be uploaded can be compared with the schema for the source table. If a schema mismatch is detected, the schema of the source table can be modified, and the upload can be continued without data loss.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/934,369, filed Sep. 22, 2022, which application claims the benefit ofpriority to U.S. Provisional Patent Application Ser. No. 63/366,034filed Jun. 8, 2022, the contents of which are incorporated herein byreference in their entireties.

TECHNICAL FIELD

The present disclosure generally relates to data systems, such as datasystems, and, more specifically, to data ingestion and copyingtechniques for different schemas.

BACKGROUND

Data systems, such as database systems, may be provided through a cloudplatform, which allows organizations and users to store, manage, andretrieve data from the cloud. A variety of techniques can be employedfor uploading and storing data in a database or table in a cloudplatform. Uploading techniques typically cannot account for differentschemas. Oftentimes, schema mismatch can lead to data loss during datatransfer.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment, according to someexample embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 is a simplified block diagram of a system for automated dataingestion, according to some example embodiments.

FIG. 5 is a schematic block diagram of a process of ingesting data intoa database, according to some example embodiments.

FIG. 6 is a flow diagram of a method for schema evolution duringauto-ingestion, according to some example embodiments.

FIG. 7 is a flow diagram of a method for detecting schema mismatches,according to some example embodiments.

FIG. 8 is a flow diagram of a method for schema evolution duringexecution of a copy command, according to some example embodiments.

FIG. 9 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Techniques for schema mismatch detection and evolution are described.When data is being uploaded into a source table, schema of the data tobe uploaded can be compared with the schema for the source table. If aschema mismatch is detected, the schema of the source table can bemodified, and the upload can be continued without data loss. The schemaevolution techniques described herein thus can improve the accuracy andspeed of data transfer in network data systems.

FIG. 1 illustrates an example shared data processing platform 100. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted from thefigures. However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of the shareddata processing platform 100 to facilitate additional functionality thatis not specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based database system 102, a cloud computing storage platform104 (e.g., a storage platform, an AWS® service, Microsoft Azure®, orGoogle Cloud Services®), and a remote computing device 106. Thenetwork-based database system 102 is a cloud database system used forstoring and accessing data (e.g., internally storing data, accessingexternal remotely located data) in an integrated manner, and reportingand analysis of the integrated data from the one or more disparatesources (e.g., the cloud computing storage platform 104). The cloudcomputing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based database system 102.While in the embodiment illustrated in FIG. 1 , a data warehouse isdepicted, other embodiments may include other types of databases orother data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based database system 102. Theremote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures.

The network-based database system 102 comprises an access managementsystem 110, a compute service manager 112, an execution platform 114,and a database 116. The access management system 110 enablesadministrative users to manage access to resources and services providedby the network-based database system 102. Administrative users cancreate and manage users, roles, and groups, and use permissions to allowor deny access to resources and services. The access management system110 can store shared data that securely manages shared access to thestorage resources of the cloud computing storage platform 104 amongstdifferent users of the network-based database system 102, as discussedin further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based database system 102. The compute service manager 112also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based databasesystem 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-N that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-N are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-N may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-N may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based database system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based database system102 and the cloud computing storage platform 104. The web proxy 120handles tasks involved in accepting and processing concurrent API calls,including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1 , data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based database system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based database system 102 to scalequickly in response to changing demands on the systems and componentswithin network-based database system 102. The decoupling of thecomputing resources from the data storage devices 124-1 to 124-Nsupports the storage of large amounts of data without requiring acorresponding large amount of computing resources. Similarly, thisdecoupling of resources supports a significant increase in the computingresources utilized at a particular time without requiring acorresponding increase in the available data storage resources.Additionally, the decoupling of resources enables different accounts tohandle creating additional compute resources to process data shared byother users without affecting the other users' systems. For instance, adata provider may have three compute resources and share data with adata consumer, and the data consumer may generate new compute resourcesto execute queries against the shared data, where the new computeresources are managed by the data consumer and do not affect or interactwith the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based database system102 is dynamic and supports regular changes to meet the current dataprocessing needs.

During typical operation, the network-based database system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1 , the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-N in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-N. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , a request processing service 202manages received data storage requests and data retrieval requests(e.g., jobs to be performed on database data). For example, the requestprocessing service 202 may determine the data necessary to process areceived query (e.g., a data storage request or data retrieval request).The data may be stored in a cache within the execution platform 114 orin a data storage device in cloud computing storage platform 104. Amanagement console service 204 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 204 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based database system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based database system 102. For example, data storage device 220may represent caches in execution platform 114, storage devices in cloudcomputing storage platform 104, or any other storage device.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , execution platform 114 includesmultiple virtual warehouses, which are elastic clusters of computeinstances, such as virtual machines. In the example illustrated, thevirtual warehouses include virtual warehouse 1, virtual warehouse 2, andvirtual warehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 124-1 to124-N and, instead, can access data from any of the data storage devices124-1 to 124-N within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-N. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

As mentioned above, data from a client storage can be uploaded to thedata warehouse. Some techniques can use a “copy” command for thistransfer. The “copy” command is typically manually performed orperformed based on a set schedule (say, every 15 minutes). However, theuse of such “copy” commands can add latency.

Consequently, latency can be improved by implementing auto-ingestiontechniques, as described in further detail below. FIG. 4 is a simplifiedblock diagram of system 400 for automated data ingestion, according tosome example embodiments. The system may include a storage 402, whichmay be provided as cloud storage (e.g., Amazon S3 storage, Azurestorage, GCP storage, etc.). The storage 402 may include client data toupload to the data warehouse.

The storage 402 may store files (or data) to be ingested into a database410. In some embodiments, the storage 402 may include a storage unit402.1, an event block 402.2, and a queue 402.3. The system may alsoinclude a deployment to ingest data in the database 410. A deploymentmay include multiple components such as a metadata store/DB, a front-endlayer, a load balancing layer, a data warehouse, etc., as discussedabove with respect to FIGS. 1-3 . The deployments may be provided aspublic or private deployments. A public deployment may be implemented asa multi-tenant environment, where each tenant or account sharesprocessing and/or storage resources. For example, in a publicdeployment, multiple accounts may share a metadata store, a front-endlayer, a load balancing layer, a data warehouse, etc. A privatedeployment, on the other hand, may be implemented as a dedicated,isolated environment, where processing and/or storage resources may bededicated.

The deployment may be communicatively coupled to the queue 402.3, andmay include an integration 404, a pipe 406, and a receiver 408.Integration 404 may be configured to receive a notification when newdata becomes available in queue 402.3. For example, the queue mayinclude a pool of Simple Queue Service™ (SQS) queues as part of anAmazon Web Services™ S3 bucket. The pool of SQS queues may be providedto client accounts to add user files to a bucket. A notification may beautomatically generated when one or more user files are added to aclient account data bucket. A plurality of customer data buckets may beprovided for each client account. The automatically generatednotification may be received by the integration 404.

For example, the integration 404 may provide information relating to anoccurrence of an event in the queue 402.3. Events may include creationof new data, update of old data, and deletion of old data. Theintegration 404 may also provide identification information for aresource associated with the event, e.g., the user file that has beencreated, updated, or deleted. The integration 404 may communicate withthe queue 402.3 because the integration 404 may be provided withcredentials for the queue 402.3, for example by an administrator and/oruser. In an embodiment, the integration 404 may poll the queue 402.3 fornotifications.

The integration 404 may deliver the notification to the pipe 406, whichmay be provided as a single pipe or multiple pipes. The pipe 406 maystore information relating to what data and the location of the data forautomatic data ingestion related to the queue 402.3.

The receiver 408 may perform the automated data ingestion, and thenstore the ingested data in the database 410. Data ingestion may beperformed using the techniques described in U.S. patent application Ser.No. 16/201,854, entitled “Batch Data Ingestion in Database Systems,”filed on Nov. 27, 2018, which is incorporated herein by reference in itsentirety, including but not limited to those portions that specificallyappear hereinafter, the incorporation by reference being made with thefollowing exception: In the event that any portion of theabove-referenced application is inconsistent with this application, thisapplication supersedes the above-referenced application.

FIG. 5 is a schematic block diagram of a process 500 of ingesting datainto a database, according to some example embodiments. The process 500begins and a storage 502 sends an ingest request, such as anotification. The storage 502 may directly or indirectly communicatewith the database system to send in the ingest request. In someembodiments, the ingest request is a notification provided by athird-party vendor storage account, or the ingest request may arise froma compute service manager polling a data lake associated with the clientaccount to determine whether any user files have been added to theclient account that have not yet been ingested into the database. Thenotification includes a list of files to insert into a table of thedatabase. The files are persisted in a queue specific to the receivingtable of the database.

The ingest request is received by a compute service manager 504. Thecompute service manager 504 identifies at step 506 a user file toingest. At step 508, the compute service manager identifies a cloudprovider type associated with the client account. At step 510, thecompute service manager 504 may assign the user file to one or moreexecution nodes, based at least in part on the detected cloud providertype, and registers at step 512 micro-partition metadata associated witha database table after the file is ingested into a micro-partition ofthe database table. The compute service manager 504 provisions one ormore execution nodes 516, 520 of an execution platform 514 to performone or more tasks associated with ingesting the user file. Such ingesttasks 518 a, 518 b, 522 a, 522 b include, for example, cutting a fileinto one or more sections, generating a new micro-partition based on theuser file, and/or inserting the new micro-partition in a table of thedatabase.

The process 500 begins an ingest task that is executed by a warehouse.The ingest task may pull user files from the queue for a database tableuntil it is told to stop doing so. The ingest task may periodically cuta new user file and add it to the database table. In one embodiment, theingest process is “serverless” in that it is an integrated serviceprovided by the database or compute service manager 504. That is, a userassociated with the client account need not provision its own warehouseor a third-party warehouse in order to perform the ingestion process.For example, the database or database provided (e.g., via instances ofthe compute service manager 504) may maintain the ingest warehouse thatthen services one or more or all accounts/customers of the databaseprovider.

In some embodiments, there may be more than one ingest task pulling froma queue for a given table, and this might be necessary to keep up withthe rate of incoming data. In some embodiments, the ingest task maydecide the time to cut a new file to increase the chances of getting anideal sized file and avoid “odd sized” files that would result if thefile size was lined up with one or more user files. This may come at thecost of added complexity as the track line number of the files consumedmust be tracked.

Data loss may occur if there is a schema mismatch between the files tobe ingested and the source table. For example, source table may have afirst schema having a first column as a string value with column name“COL_A” and a second column as an integer value with column name“COL_B.” A file to be ingested may, however, have an additional thirdcolumn as a timestamp value with column name “COL_C.” In conventionaluploading techniques, the data in the third column in the file to beingested may be lost in the data transfer because the source table doesnot have a corresponding third column “COL_C.” Described below aretechniques for schema evolution to prevent data loss in the event of aschema mismatch.

Auto ingestion, as described herein, is a task-based service, which isdifferent than other queries such as copy commands. To allow for schemaevolution, a source table (also referred to as a target table) may bedesignated for enabling schema evolution. As described in further below,schema evolution can change the schema of the source table during datatransfer. A user with specified privileges can be allowed to enableschema evolution, such as a table owner. Moreover, a pipe owner can haveschema evolution privileges for the source table. The schema evolutiontechniques may compare column names, so a “MATCH_BY_COLUMN_NAME” featuremay be specified in a definition of a pipe.

FIG. 6 is a flow diagram of a method 600 for schema evolution duringauto-ingestion, according to some example embodiments. Method 600 may beperformed using the auto ingestion techniques described above withreference to FIGS. 4 and 5 . That is, a compute service manager, asdescribed herein, may receive notifications of files to be ingested andcreate a query plan for ingesting those files. The compute servicemanager may assign ingest tasks to execution nodes of one or moreexecution platforms (XPs) as described above.

At operation 602, a schema mismatch may be detected. For example, an XPmay detect a schema mismatch between the schema of an external file tobe ingested and the source table. Different techniques for detectingschema mismatch may be used, as described in further detail below.Different schema mismatches may be detected. For example, the file to beingested may include one or more additional columns not in the sourcetable. As another example, the file to be ingested may have null valuesin a designated non-nullable column in the source table.

At operation 604, a special incident (e.g., an error message) may begenerated indicating the schema mismatch. For example, the XP maygenerate the error message and may transmit the error message to thecompute service manager through an API. The error message may includeinformation regarding the schema mismatch. For example, the errormessage may include information indicating what type of schema mismatchwas detected and may include what schema changes may be needed to beperformed on the source table to resolve the schema mismatch.

At operation 606, the schema for the source table may be modified basedon the error message. For example, the compute service manager mayconstruct a Data Definition Language (DDL) statement to modify theschema for the source table based on the information in the errormessage. For adding a new column, a DDL statement of “ALTER TABLE ADDCOLUMN” may be executed. In some examples, an internal code equivalentto the DDL statement may be run; in this case, the DDL statement may notappear in the query history. Adding columns can result in an extendedtable, which can decrease efficient data processing. Therefore, a limitmay be set for the number of columns that can be added to the sourcetable for schema evolution. For example, a limit of 10 additionalcolumns per schema can be set. This limit may be adjustable. All newcolumns are nullable to account for the lack of data in the new columnfor the data already in the source table, for example.

For changing the nullability of a column, another corresponding altercommand may be executed. In some examples, an internal commandequivalent may be run so that the overall schema evolution can beexecuted as a single command. The command may change a column fromnon-nullable to nullable. That is, the column may now contain nullvalues (e.g., no data). The change to the source table schema (i.e.,modified or evolved schema) may be stored in the metadata associatedwith the source table.

At operation 608, the affected ingest tasks may be quarantined in aqueue. Other ingest tasks not affected by the schema mismatch maycontinue to be executed. The quarantined ingest tasks may be consideredan ingest failure at this moment when placed in the queue so that thequarantined ingest tasks can be retried. The user, however, may not beaware of this ingest failure.

At operation 610, the quarantined tasks may be retried using themodified schema (or evolved schema). That is, the compute servicemanager may recompile the query plan for the quarantined tasks using themodified schema. The assigned XP(s) may then execute the ingest tasksusing the modified schema for its next schema mismatch check. If thereis no mismatch found, the files associated with the quarantined ingesttasks may be ingested into the source table.

In some embodiments, the quarantined tasks may be retriggeredautomatically by a next iteration of a maintenance cycle. Themaintenance cycle may be performed periodically (e.g., every fewseconds, minutes, etc.) for tasks that need to be re-executed due tosome initial failures. The quarantined tasks may be placed in a queue,as described above (e.g., QueueExecutor). When the schema is modifiedand a new query plan is compiled, the quarantined tasks may berestarted. For example, a command to clean up the queue (e.g.,QueueExecutor:cleanup()) may be executed to stop the queue and restartit automatically in the next maintenance cycle.

In some embodiments, errors may occur due to schema evolution. Someerrors can be recoverable. One example of a recoverable error isconcurrently adding the column with the same name and data type.Consider two tasks, task_1 and task_2, that tried to add the same columnC3 with the same type of STRING at the same time. The second DDL fromtask_2 will fail since the column is already added by the first DDL.However, if task_2 retries, it will eventually succeed since there won'tbe a schema mismatch (C3 is already added).

In some embodiments, schema evolution may be indeterministic. The casemight not be deterministic since schema evolution can be a first-infirst-serve behavior. Consider an example where the user loads two filesfor auto ingestion. One column named “COL_TO_ADD” is a new column inboth files. However, the type of the column is “STRING” in one file and“INTEGER” in another. In this case, the first file scanned will triggerthe schema evolution and the second file will cause an error since thecolumn “COL_TO_ADD” already exists with a different data type. As aresult, the evolved column in the table might be either a “STRING” typeor an “INTEGER” type.

FIG. 7 is a flow diagram of a method 700 for detecting schemamismatches, according to some example embodiments. Method 700 may beperformed by an XP executing one or more ingest tasks as describedabove. Method 700 may be performed before a file is ingested.

At operation 702, a file format for the file to be ingested is detected.For example, the XP may determine if the file format belongs to a firstset of file types or a second set of file types. In some embodiments, apipe is configured for a specific format and therefore the file formatmay be detected based on the pipe configuration. The first set of filetypes may include schema information about the file to be ingested inits metadata information or have designated schema information. Thefirst set of file types may include file types such as Parquet, ORC, andAvro. The second set of file types may not include designated schemainformation or include schema information in its metadata information.The second set of file types may include file types such as CSV, Json,and XML.

If the detected file type belongs to the first set of file types, atoperation 704, the metadata/schema information may be scanned for thefile (e.g., ScanExternalSchemaRso). At operation 706, the scanner maygather or retrieve schema information for the file.

If the detected file type belongs to the second set of file types, atoperation 708, a select number of rows from the file to be ingested maybe scanned (e.g., ScanExternalRso). At operation 710, the scanned rowsmay be used to infer the schema of the file.

At operation 712, the schema of the source table, which may be retrievedfrom the metadata associated with the source table, may be compared tothe schema of the file to be ingested (either retrieved frommetadata/schema information or inferred as described above). Atoperation 714, based on the comparison, a schema mismatch may bedetected. Different schema mismatches may be detected. For example, thefile to be ingested may include one or more additional columns not inthe source table. As another example, the file to be ingested may havenull values in a designated non-nullable column in the source table.

Schema evolution, as described herein, may also be used when executing“copy” commands to upload files to a source table. FIG. 8 is a flowdiagram of a method 800 for schema evolution during execution of a copycommand, according to some example embodiments.

At operation 802, a copy command may be received and a query plan toexecute the copy command may be generated. For example, a computeservice manager may generate the query plan and assign one or more XPsto execute the query plan.

At operation 804, a schema mismatch may be detected. For example, anexecution platform may detect a schema mismatch between the schema of afile to be copied and the source table. Different techniques fordetecting schema mismatch may be used, as described in further detailabove. Different schema mismatches may be detected. For example, thefile to be copied may include one or more additional columns not in thesource table. As another example, the file to be copied may have nullvalues in a designated non-nullable column in the source table.

At operation 806, a special incident (e.g., an error message) may begenerated indicating the schema mismatch. For example, the XP maygenerate the error message and may transmit the error message to thecompute service manager through an API. The error message may includeinformation regarding the schema mismatch. For example, the errormessage may include information indicating what type of schema mismatchwas detected and may include what schema changes may be needed to beperformed on the source table to resolve the schema mismatch.

At operation 808, the schema for the source table may be modified basedon the error message. For example, the compute service manager mayconstruct a DDL statement (or internal code equivalent) to modify theschema for the source table based on the information in the errormessage. For adding a new column, a DDL statement (or internal codeequivalent of a single command) of “ALTER TABLE ADD COLUMN” may beexecuted. Adding columns can result in an extended table, which candecrease efficient data processing. Therefore, a limit may be set forthe number of columns that can be added to the source table for schemaevolution. For example, a limit of 10 additional columns per schema canbe set. This limit may be adjustable. All new columns are nullable toaccount for the lack of data in the new column for the data already inthe source table, for example.

For changing the nullability of a column, another corresponding altercommand may be executed. The command may change a column fromnon-nullable to nullable. That is, the column may now contain nullvalues (e.g., no data). The change to the source table schema (i.e.,modified or evolved schema) may be stored in the metadata associatedwith the source table.

At operation 810, the original copy command may be set as a failedcommand/query. At operation 812, the failed copy command/query may bere-tried, but this time using the modified schema. For example, acompute service manager may generate a new query plan using the modifiedschema. The assigned XP(s) may then execute the new query plan for itsnext schema mismatch check. If there is no mismatch found, the filesassociated with the re-tried copy command may be copied into the sourcetable. In some embodiments, a different compute service manager may beused to re-try the copy command after the schema has been modified.

FIG. 9 illustrates a diagrammatic representation of a machine 900 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 900 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions916 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 916 may cause the machine 900 to execute any one ormore operations of any one or more of the methods described herein. Asanother example, the instructions 916 may cause the machine 900 toimplement portions of the data flows described herein. In this way, theinstructions 916 transform a general, non-programmed machine into aparticular machine 900 (e.g., the remote computing device 106, theaccess management system 110, the compute service manager 112, theexecution platform 114, the access management system 118, the Web proxy120) that is specially configured to carry out any one of the describedand illustrated functions in the manner described herein.

In alternative embodiments, the machine 900 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 900 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 916, sequentially orotherwise, that specify actions to be taken by the machine 900. Further,while only a single machine 900 is illustrated, the term “machine” shallalso be taken to include a collection of machines 900 that individuallyor jointly execute the instructions 916 to perform any one or more ofthe methodologies discussed herein.

The machine 900 includes processors 910, memory 930, and input/output(I/O) components 950 configured to communicate with each other such asvia a bus 902. In an example embodiment, the processors 910 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 912 and aprocessor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors 910 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 916 contemporaneously. AlthoughFIG. 9 shows multiple processors 910, the machine 900 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 930 may include a main memory 932, a static memory 934, and astorage unit 936, all accessible to the processors 910 such as via thebus 902. The main memory 932, the static memory 934, and the storageunit 936 store the instructions 916 embodying any one or more of themethodologies or functions described herein. The instructions 916 mayalso reside, completely or partially, within the main memory 932, withinthe static memory 934, within the storage unit 936, within at least oneof the processors 910 (e.g., within the processor's cache memory), orany suitable combination thereof, during execution thereof by themachine 900.

The I/O components 950 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 950 thatare included in a particular machine 900 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 950 mayinclude many other components that are not shown in FIG. 9 . The I/Ocomponents 950 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 950 mayinclude output components 952 and input components 954. The outputcomponents 952 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 954 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 or devices 970 via a coupling982 and a coupling 972, respectively. For example, the communicationcomponents 964 may include a network interface component or anothersuitable device to interface with the network 980. In further examples,the communication components 964 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 970 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 900 may correspond to any one of the remote computing device106, the access management system 118, the compute service manager 112,the execution platform 114, the Web proxy 120, and the devices 970 mayinclude any other of these systems and devices.

The various memories (e.g., 930, 932, 934, and/or memory of theprocessor(s) 910 and/or the storage unit 936) may store one or more setsof instructions 916 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 916, when executed by the processor(s) 910,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 980may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 980 or a portion of the network980 may include a wireless or cellular network, and the coupling 982 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 982 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 916 may be transmitted or received over the network 980using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916 may be transmitted or received using a transmission medium via thecoupling 972 (e.g., a peer-to-peer coupling) to the devices 970. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 916 for execution by the machine 900, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1. A method comprising: detecting, by one or more executionnodes, a schema mismatch between a file to be ingested and a sourcetable; generating, by the one or more execution nodes, a message basedon detecting the schema mismatch and transmitting the message to acompute service manager; modifying, by the compute service manager, aschema for the source table based on the message; quarantining, by thecompute service manager, at least one ingest task associated with thefile; compiling, by the compute service manager, a modified query planfor the at least one quarantined ingest task based on the modifiedschema of the source table; and executing, by the one or more executionnodes, the quarantined ingest tasks based on the modified query plan toingest the file into the source table.

Example 2. The method of example 2, further comprising: receiving, bythe compute service manager, a notification regarding the file to beingested into the source table; generating, by the compute servicemanager, a query plan for ingesting the file; and assigning, by thecompute service manager, the at least one ingest task based on the queryplan to the one or more execution platforms.

Example 3. The method of any of examples 1-2, wherein the schemamismatch is detected based on the file including one or more columns notin the source table.

Example 4. The method of any of examples 1-3, wherein the schemamismatch is detected based on the file including null values in adesignated non-nullable column in the source table.

Example 5. The method of any of examples 1-4, wherein the messageincludes information regarding a type of schema mismatch.

Example 6. The method of any of examples 1-5, wherein modifying theschema for the source table includes constructing a data definitionlanguage statement to add or alter a column based on information in themessage.

Example 7. The method of any of examples 1-6, wherein detecting theschema mismatch includes scanning schema information saved in metadatafor the file.

Example 8. The method of any of examples 1-7, wherein detecting theschema mismatch includes: scanning a set of rows from the file, anddetermining a schema for the file based on the scanned set of rows.

Example 9. A system comprising: one or more processors of a machine; anda memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations implementing any oneof example methods 1 to 8.

Example 11. A machine-readable storage device embodying instructionsthat, when executed by one or more machines, cause the one or moremachines to perform operations implementing any one of example methods 1to 8.

What is claimed is:
 1. A method comprising: receiving, by a computeservice manager, a copy command to copy a file into a source table;generating, by the compute service manager, a query plan to execute thecopy command; assigning, by the compute service manager, one or moreexecution nodes to execute the query plan; detecting, by the one or moreexecution nodes, a schema mismatch between the file and the sourcetable; generating, by the one or more execution nodes, a message basedon detecting the schema mismatch; modifying, by the compute servicemanager, a schema for the source table based on the message; compiling,by the compute service manager, a modified query plan for the copycommand based on the modified schema of the source table; and executing,by the one or more execution nodes, the copy command based on themodified query plan to copy the file into the source table.
 2. Themethod of claim 1, further comprising: setting the copy command as afailed command; and re-trying the failed command, wherein the modifiedquery plan is compiled based on re-trying the failed command.
 3. Themethod of claim 1, wherein the schema mismatch is detected based on thefile including one or more columns not in the source table.
 4. Themethod of claim 1, wherein the schema mismatch is detected based on thefile including null values in a designated non-nullable column in thesource table.
 5. The method of claim 1, wherein the message includesinformation regarding a type of schema mismatch.
 6. The method of claim1, wherein modifying the schema for the source table includesconstructing a data definition language statement to add or alter acolumn based on information in the message.
 7. The method of claim 1,wherein detecting the schema mismatch includes scanning schemainformation saved in metadata for the file.
 8. The method of claim 1,wherein detecting the schema mismatch includes: scanning a set of rowsfrom the file, and determining a schema for the file based on thescanned set of rows.
 9. A machine-storage medium embodying instructionsthat, when executed by one or more machines, cause the one or moremachines to perform operations comprising: receiving, by a computeservice manager, a copy command to copy a file into a source table;generating, by the compute service manager, a query plan to execute thecopy command; assigning, by the compute service manager, one or moreexecution nodes to execute the query plan; detecting, by the one or moreexecution nodes, a schema mismatch between the file and the sourcetable; generating, by the one or more execution nodes, a message basedon detecting the schema mismatch; modifying, by the compute servicemanager, a schema for the source table based on the message; compiling,by the compute service manager, a modified query plan for the copycommand based on the modified schema of the source table; and executing,by the one or more execution nodes, the copy command based on themodified query plan to copy the file into the source table.
 10. Themachine-storage medium of claim 9, further comprising: setting the copycommand as a failed command; and re-trying the failed command, whereinthe modified query plan is compiled based on re-trying the failedcommand.
 11. The machine-storage medium of claim 9, wherein the schemamismatch is detected based on the file including one or more columns notin the source table.
 12. The machine-storage medium of claim 9, whereinthe schema mismatch is detected based on the file including null valuesin a designated non-nullable column in the source table.
 13. Themachine-storage medium of claim 9, wherein the message includesinformation regarding a type of schema mismatch.
 14. The machine-storagemedium of claim 9, wherein modifying the schema for the source tableincludes constructing a data definition language statement to add oralter a column based on information in the message.
 15. Themachine-storage medium of claim 9, wherein detecting the schema mismatchincludes scanning schema information saved in metadata for the file. 16.The machine-storage medium of claim 9, wherein detecting the schemamismatch includes: scanning a set of rows from the file, and determininga schema for the file based on the scanned set of rows.
 17. A systemcomprising: at least one hardware processor; and at least one memorystoring instructions that, when executed by the at least one hardwareprocessor, cause the at least one hardware processor to performoperations comprising: receiving, by a compute service manager, a copycommand to copy a file into a source table; generating, by the computeservice manager, a query plan to execute the copy command; assigning, bythe compute service manager, one or more execution nodes to execute thequery plan; detecting, by the one or more execution nodes, a schemamismatch between the file and the source table; generating, by the oneor more execution nodes, a message based on detecting the schemamismatch; modifying, by the compute service manager, a schema for thesource table based on the message; compiling, by the compute servicemanager, a modified query plan for the copy command based on themodified schema of the source table; and executing, by the one or moreexecution nodes, the copy command based on the modified query plan tocopy the file into the source table.
 18. The system of claim 17, theoperations further comprising: setting the copy command as a failedcommand; and re-trying the failed command, wherein the modified queryplan is compiled based on re-trying the failed command.
 19. The systemof claim 17, wherein the schema mismatch is detected based on the fileincluding one or more columns not in the source table.
 20. The system ofclaim 17, wherein the schema mismatch is detected based on the fileincluding null values in a designated non-nullable column in the sourcetable.
 21. The system of claim 17, wherein the message includesinformation regarding a type of schema mismatch.
 22. The system of claim17, wherein modifying the schema for the source table includesconstructing a data definition language statement to add or alter acolumn based on information in the message.
 23. The system of claim 17,wherein detecting the schema mismatch includes scanning schemainformation saved in metadata for the file.
 24. The system of claim 17,wherein detecting the schema mismatch includes: scanning a set of rowsfrom the file, and determining a schema for the file based on thescanned set of rows.